def pso_esn_parameters_for_scad(x):
# 0: tao, 1:c0, 2:IC_s,3:IC_e 4:IS_s,5:IS_e, ,6:teacher sacling,7:teacher shift
# 0:IC_s,1:IC_e, 2:IS_s,3:IS_e, 4:teacher sacling,5:teacher shift,6: tao, 7:c0,
ic_s =x[0]
ic_e =x[1]
is_s = x[2]
is_e = x[3]
teacher_scaling = x[4]
teacher_shift = x[5]
tao = x[6]
c0 = x[7]
esn = ESN(n_inputs = 2,
n_outputs = 1,
n_reservoir = n_reservoir,
spectral_radius = spectral_radius,
sparsity = sparsity,
noise = noise,
input_shift = [is_s,is_e],#[0,0]
input_scaling =[ic_s,ic_e],# [0.01, 3]
teacher_scaling = teacher_scaling,#1.12,
teacher_shift = teacher_shift,#-0.7,
out_activation = np.tanh,
inverse_out_activation = np.arctanh,
random_state = rng,
silent = False)
esn.penal_tao =tao
esn.penal_c0 = c0
internal_states,transient = esn.train_reservior(train_ctrl,train_output)
pred_train = esn.train_readout_with_scad(internal_states,train_output,transient)
pred_test = esn.predict(test_ctrl)
#test_error_rate= np.sqrt(np.mean((pred_test - test_output)**2))
test_error_rate= np.sqrt((pred_test - test_output)**2)
#get function name as title
title = inspect.stack()[0][3]
print "#### {} ## train_error:{},test_error:{}".format(title,esn.train_error_rate,test_error_rate)
return test_error_rate
这里画一个原始测试序列的图和一个test_output的图。
def compair_readout():
esn = ESN(n_inputs = 8,
n_outputs = 1,
n_reservoir = n_reservoir,
spectral_radius = spectral_radius,
sparsity = sparsity,
noise = noise,
input_shift = [0.51293657,0.51293657,0.51293657,0.51293657,0.03489584,0.03489584,0.03489584,0.03489584],
input_scaling = [0.18636639,0.18636639,0.18636639,0.18636639, 0.11791364, 0.11791364, 0.11791364, 0.11791364],
teacher_scaling = 1.45377531,
teacher_shift = -0.7997228,
out_activation = np.tanh,
inverse_out_activation = np.arctanh,
random_state = rng,
silent = False)
esn = ESN(n_inputs = 8,
n_outputs = 1,
n_reservoir = n_reservoir,
spectral_radius = spectral_radius,
sparsity = sparsity,
noise = noise,
input_shift = [0,0,0,0,0,0,0,0],
input_scaling = [0.01,0.01,0.01,0.01,0.01,0.01,3,3],
teacher_scaling = 1.12,
teacher_shift = -0.7,
out_activation = np.tanh,
inverse_out_activation = np.arctanh,
random_state = rng,
silent = False)
esn.penal_tao =0.57201544
esn.penal_c0 = 3.76108161
esn.penal_tao = 0.1
esn.penal_c0 = 3.7
#pred_train = esn.fit(train_ctrl,train_output,inspect=True)
internal_states,transient = esn.train_reservior(train_ctrl,train_output)
# esn_Lasso = copy.deepcopy(esn)
# esn_Ridge = copy.deepcopy(esn)
# esn_ElasticNet = copy.deepcopy(esn)
esn_SCAD = copy.deepcopy(esn)
esn.dump_parameters()
# print "####pin"
# pred_train = esn.train_readout_with_pin(internal_states,train_output,transient)
# test_error("pinv",esn,pred_train)
# exit()
#
# print "####ridge"
# pred_train = esn_Ridge.train_readout_with_ridge(internal_states,train_output,transient)
# test_error("pinv",esn_Ridge,pred_train)
#
# print "####Lasso"
# pred_train = esn_Lasso.train_readout_with_lasso(internal_states,train_output,transient)
# test_error("pinv",esn_Lasso,pred_train)
#
# print "####ElasticNet"
# pred_train = esn_ElasticNet.train_readout_with_elasticnet(internal_states,train_output,transient)
# test_error("pinv",esn_ElasticNet,pred_train)
print "####SCAD"
pred_train = esn_SCAD.train_readout_with_scad(internal_states,train_output,transient)
test_error("pinv",esn_SCAD,pred_train)
